Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Data accuracy vs. data quality Data accuracy and data quality are related concepts but they are not synonymous.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Towards Data Science

APRIL 6, 2023

Principles, practices, and examples for ensuring high quality data flows Source: DreamStudio (generated by author) Nearly 100% of companies today rely on data to power business opportunities and 76% use data as an integral part of forming a business strategy. Or even just a little bit.

Unstructured Data 76

Unstructured Data Metadata Data Coding 76

Monte Carlo

JANUARY 25, 2023

There is, however, an added dimension to this relationship: data producers are often consumers of upstream data sources. Data warehouse producers wear both hats working with upstream producers so they can consume high-quality data and producing high-quality data to provide to their consumers.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Monte Carlo

AUGUST 30, 2023

Your test passes when there are no rows returned, which indicates your data meets your defined conditions. Run the test again to validate that the initial problem is solved and that your data meets your quality and accuracy standards. With dbt tests, no news is good news. Make sure logs are accessible for future reference.

SQL 52

SQL Datasets Database High Quality Data 52

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Airbnb Tech

NOVEMBER 28, 2023

To fully enable this incentivization approach, we believed it would be paramount to introduce the concept of a data quality score directly tied to data assets. We identified the following objectives for the score: Evolve our understanding of data quality beyond a simple binary definition (certified vs uncertified).

Data Warehouse 98

Data Warehouse Metadata Data Certification 98